Abstract
In this population-based case–control study with 325 Caucasian renal cell carcinoma (RCC) patients and 335 controls matched to cases by age, gender and county of residence, we evaluated the associations between 13 potential functional polymorphisms in nine major nucleotide excision repair (NER) genes and RCC risk. In individual single nucleotide polymorphism analysis, after adjustment for multiple comparisons, a significantly decreased RCC risk was observed for the heterozygous genotype of XPD Asp312Asn [odds ratio (OR) = 0.62; 95% confidence interval (CI): 0.43–0.90] and for the heterozygous and homozygous variant genotypes combined in a dominant model (OR = 0.64; 95% CI: 0.46–0.89). The heterozygous AG genotype of XPA 5′untranslated region was at 1.78-fold increased risk (95% CI: 1.18–2.69) and the risk reached 2.43-fold (95% CI: 1.57–3.75) for the homozygous variant GG genotype; the risk was significant both in the dominant model and in the recessive model. In joint analysis, compared with individuals with fewer than five adverse alleles, individuals with five (OR = 1.17; 95% CI: 0.71–1.93), six (OR = 1.66; 95% CI: 1.03–2.67), seven or more (OR = 1.85; 95% CI: 1.16–2.95) exhibited a progressively increased risk of RCC (P for trend = 0.004). Further, there were significant interactions between NER pathway genes and sex, hypertension and obesity (all P for interaction <0.05). Our results strongly support that common sequence variants of the NER pathway genes predispose susceptible individuals to increased risk of RCC and that the association may be modified by gender, history of hypertension and obesity. These results need to be replicated in larger studies.
Introduction
Renal cell carcinoma (RCC) represents the third leading cause of death among genitourinary malignancies. In 2008, it's estimated that 54,390 new cases of kidney and renal pelvis cancers are expected and that 13,010 deaths will occur in the USA (1). Major risk factors linked to this disease include cigarette smoking, obesity and a history of hypertension, along with some other less certain factors such as diet, occupational exposures, physical activity, alcohol consumption and family history of cancer (2–4).
The fact that only a small fraction of smokers develop RCC implies the influence of host factors on individual susceptibility. Further, previous work suggests that body size and fat content may influence carcinogen–DNA adduct levels (5,6). Interindividual differences in susceptibility to RCC may be attributed to genetic alterations in major DNA repair pathways that maintain genomic integrity. Four major DNA repair pathways exist in mammalian cells: base excision repair, nucleotide excision repair (NER), double-strand break repair and mismatch repair (7). The NER pathway recognizes and repairs a variety of bulky DNA adducts, ultraviolet-induced pyrimidine dimmers, cross-links and oxidative damage. The NER pathway involves sequential steps of reaction, including DNA damage recognition, incision of damaged DNA, repair of the gapped DNA and DNA ligation (8). Many polymorphisms in NER genes have been studied in terms of their associations with risk of various cancers (9–11), including a couple of small studies in RCC (12,13). In this study, we applied a pathway-based multigenic approach, which has been shown to augment the effects of individual polymorphisms and enhance cancer risk assessment (9), and to investigate the individual and joint effects of 13 potential functional polymorphisms in nine major NER repair genes (ERCC1, XPD, XPF, XPG, XPC, RAD23B, CCNH, ERCC6, XPA) on RCC risk. We also used classification and regression tree (CART) analysis to explore high-order gene–gene interactions in modulating RCC risk.
Materials and methods
Study population and epidemiologic data collection
Incident RCC cases were recruited from The University of Texas M. D. Anderson Cancer Center in Houston, Texas. M. D. Anderson Cancer Center staff interviewers identified RCC cases through a daily review of computerized appointment schedules for the Departments of Urology and Genitourinary Medical Oncology. All cases were individuals with newly diagnosed, histologically confirmed RCC. There was no age, gender, ethnicity or cancer stage restrictions on recruitment. To be eligible, the cases must be residents of Texas. Healthy control subjects without a history of cancer, except non-melanoma skin cancer, were identified and recruited using the random digit dialing method (14). In random digit dialing, randomly selected phone numbers from household were used to contact potential control volunteers in the same residency of cases accordingly to the telephone directory listings. Controls must have lived for at least 1 year in the same county or socioeconomically matched surrounding counties in Texas as the case resides and have no prior history of cancer. The controls were frequency matched to the cases by age (±5 years), sex, ethnicity and county of residence. This population-based RCC case–control study started subject recruitment in 2002 and is currently ongoing. The refusal rates for the cases and controls were 13% and 30%, respectively.
For both cases and controls, after obtaining written informed consent, trained M.D. Anderson staff interviewers administered a 45 min risk factor questionnaire to study participants. Data were collected on demographic characteristics (age, gender, ethnicity, etc.), occupation history, tobacco use history, medical history and family history of cancer. At the end of the interview, a 40 ml blood sample was drawn into coded heparinized tubes and delivered to laboratory for molecular analysis. The study was approved by the Institutional Review Board of M.D. Anderson Cancer Center. An individual who had never smoked or had smoked <100 cigarettes in his or her lifetime was defined as a never smoker. An individual who had smoked at least 100 cigarettes in his or her lifetime but had quit >12 months before diagnosis (for cases) or before the interview (for controls) was coded as a former smoker. Current smokers were those who were currently smoking or had quit <12 months before diagnosis (for cases) or before the interview (for controls).
Genotyping
Genotyping procedures for NER single nucleotide polymorphisms (SNPs) were described previously (9,15). The selected SNPs include all published common non-synonymous SNPs (minor allele frequency > 5%) in these genes and a few other potential functional SNPs (5′untranslated region (UTR), splice site and one synonymous SNP) that have been investigated in previous cancer association studies. Genomic DNA was isolated from peripheral blood lymphocytes using the QIAamp DNA blood maxi kit (QIAGEN, Valencia, CA). Except for XPA A23G, XPD Asp321Asn and XPC-PAT polymorphism, which were genotyped using polymerase chain reaction–restriction fragment length polymorphism, genotyping was performed using the Taqman method with a 7900 HT sequence detector system (Applied Biosystems, Foster City, CA) for the following NER SNPs: ERCC1 (G/T at 3′UTR, rs3212986), XPD Lys751Gln, XPG Asp1104His, XPC Lys939Gln, XPC Ala499Val, RAD23B Ala249Val, CCNH Val270Ala, ERCC6 Met1097Val, XPF Pro662Ser and ERCC6 Arg1230Pro. All theses SNPs selected have been reported in literature to have potential functional significance. In our laboratory, strict quality control procedures are implemented to ensure high genotyping accuracy. Positive controls and negative controls were included in each plate. Five percent of the samples were randomly selected and run in duplicates to ensure accuracy of the genotyping. Laboratory staff were blind of case–control status of the samples.
Statistical analysis
The Pearson’s χ2 test and the Wilcoxon rank sum test were used to test the differences in characteristics between cases and controls. The Hardy–Weinberg equilibrium was tested using a goodness of fit χ2 test. Multivariate unconditional logistic regression analysis was performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for age, sex, smoking status, history of hypertension and obesity. The combined effects of minor alleles were analyzed as a categorical variable by grouping the subjects according to the number of minor alleles in each pathway. We treated the minor allele at each locus as the ‘adverse’ allele and tallied the total number of adverse alleles for each individual. For genes with multiple SNPs assayed, only one SNP was included in this pathway analysis. A trend test was performed to test for a linear trend in the ORs. A likelihood ratio test was used to test for interactions among variables. This test compares the likelihood of a full model including the interaction term with a reduced model without the interaction term. All statistical analyses were two sided. All analyses were performed using the Intercooled Stata 8.0 statistical software package (Stata Co., College Station, TX).
To account for multiple comparisons, we used the false discovery rate (FDR) function in the R software (version 2.5) to estimate the FDR based on the Benjamini–Hochberg method (16). We calculated the FDR-adjusted P values at 5% level to assess whether the resulting P values were still significant after adjusting for multiple comparisons.
Haplotype and diplotype frequencies were analyzed using the HelixTree Genetics Analysis Software (Golden Helix, Bozeman, MT). Haplotypes were inferred using the expectation–maximization algorithm implemented in the Helix Tree software. Haplotypes with a probability of <95% were excluded from the final analysis. The adjusted ORs and 95% CI for each haplotype and/or diplotype were calculated using multivariate logistic regression using the most abundant haplotype and/or diplotype as the reference group.
To explore gene–gene interaction in modulating RCC risk, we applied a recursive partitioning technique (17). The recursive partitioning was derived from the methodology of CART. In CART, a tree-based model is created by recursive partitioning the data and allows identify effect modifications between variables that are less visible by traditional logistic regression. The algorithm splits the study samples into a number of homogenous subgroups based on risk factors. The final model is a tree structure with terminal nodes defining a range of risk subgroups. CART analysis was performed using the RPART package in the R software (version 2.5).
Results
In this report, we restricted our analysis to Caucasians only due to the small sample size of the minority cases. There were 325 RCC Caucasian cases and 335 matched controls (Table I). There were no significant differences in the distribution of sex between cases and controls (P = 0.16). The mean ages of the cases and controls were 59.36 and 59.73 years, respectively (P = 0.66). There were no significant differences in the distribution of current, former and never smokers in cases and controls (P = 0.13). Among ever smokers, smoking duration, number of cigarettes per day and pack-year of smoking were similar between the two groups (P = 0.87, 0.82 and 0.95, respectively). A total of 54.95% of the cases had a history of hypertension as compared with only 42.81% in controls (P = 0.002). The mean body mass index was significantly higher in cases than in controls (30.13 versus 28.14, P = 0.003) (Table I).
Table I.
Selected characteristics of cases and controls
| Cases | Controls | P-value | |
| N = 325 | N = 335 | ||
| Sex, N (%) | |||
| Male | 216 (66.46) | 205 (61.19) | |
| Female | 109 (33.54) | 130 (38.81) | 0.16 |
| Age, mean (SD) | 59.36 (10.81) | 59.73 (10.80) | 0.66 |
| Smoking status, N (%) | |||
| Never | 164 (50.46) | 150 (44.78) | |
| Former | 119 (36.62) | 124 (37.01) | |
| Current | 42 (12.92) | 61 (18.21) | 0.13 |
| Pack-years, median (range) | 22 (0.25–150) | 19.25 (0.2–133) | 0.95 |
| Smoking duration, median (range) | 25 (1–62) | 24 (1–58) | 0.87 |
| Number of cigarettes per day, median (range) | 20 (1–80) | 20 (1–80) | 0.82 |
| Hypertension | |||
| Yes | 172 (54.95) | 143 (42.81) | |
| No | 141 (45.05) | 191 (57.19) | 0.002 |
| BMI (kg/m2) mean (SD) | 30.13 (10.43) | 28.14 (5.77) | 0.003 |
BMI, body mass index.
Except for the XPA SNP (rs1800975), the genotype distributions of all other SNPs were in agreement with Hardy–Weinberg equilibrium (data not shown) in controls. Deviation from the Hardy–Weinberg equilibrium of the XPA SNP was also reported in other studies (18). The deviation should not occur due to genotyping error because in our laboratory, strict quality control procedures were implemented to ensure high genotyping accuracy. For example, positive controls and negative controls were included in each plate and 5% of the samples were randomly selected and run in duplicates to ensure high concordance in genotyping. Table II showed RCC risks associated with each single polymorphism. We presented each of the associations in additive, dominant and recessive models (for XPF Pro662Ser, only dominant model was presented due to the small sample size of heterozygous and homozygous variant genotypes). When compared with the homozygous wild-type reference group, a significantly decreased RCC risk was observed for the heterozygous genotype (GA) of XPD Asp312Asn (rs1052559) (OR = 0.62; 95% CI: 0.43–0.90). When the heterozygous and the homozygous variant genotypes of the XPD Asp312Asn were combined (GA + AA) in the dominant model, the OR was 0.64 (95% CI: 0.46–0.89). Similarly, the heterozygous ERCC6 Met1097Val (rs2228526) (AG) exhibited a significantly increased risk (OR = 1.46; 95% CI: 1.03–2.07) and individuals carrying at least one copy of the variant G allele (AG + GG) had an OR of 1.42 (95% CI: 1.02–1.98). In addition, compared with the XPA 5′UTR (rs1800975) AA genotype, the heterozygous AG genotype was at 1.78-fold increased risk (95% CI: 1.18–2.69) and the risk reached 2.43-fold (95% CI: 1.57–3.75) for the homozygous variant GG genotype. The ORs were also significant in the dominant model (OR = 2.04; 95% CI: 1.40–2.97) and in the recessive model (OR = 1.77; 95% CI: 1.24–2.54) (Table II). After adjustment for multiple comparisons, the ORs for XPD rs1799793 and XPA rs1800975 remained significant, whereas the ORs for ERCC6 rs2228526 did not reach statistical significance at the FDR-adjusted 5% level.
Table II.
The association between single NER SNPs and RCC risk
| Cases | Controls | ||||
| N (%) | N (%) | Adjusted ORa | 95% CI | P-value | |
| ERCC1 3′UTR (rs321986) | |||||
| GG | 182 (56.62) | 187 (56.84) | Ref. | ||
| GT | 120 (37.27) | 121 (36.78) | 1.06 | 0.75–1.48 | 0.75 |
| TT | 20 (6.21) | 21 (6.38) | 0.95 | 0.49–1.87 | 0.89 |
| P for trend | 0.83 | ||||
| (GT + TT) versus GG | 1.04 | 0.75–1.44 | 0.81 | ||
| TT versus (GG + GT) | 1.06 | 0.54–2.09 | 0.85 | ||
| XPD Lys751Gln (rs1052559) | |||||
| AA | 144 (45.00) | 137 (42.15) | Ref. | ||
| AC | 132 (41.25) | 140 (43.08) | 0.91 | 0.65–1.30 | 0.62 |
| CC | 44 (13.75) | 48 (14.77) | 0.89 | 0.55–1.45 | 0.64 |
| P for trend | 0.87 | ||||
| (AC + CC) versus AA | 0.91 | 0.66–1.26 | 0.56 | ||
| CC versus (AA + AC) | 1.01 | 0.63–1.62 | 0.96 | ||
| XPD Asp312Asn (rs1799793) | |||||
| GG | 147 (48.36) | 127 (38.72) | Ref. | ||
| GA | 103 (33.88) | 139 (42.38) | 0.62 | 0.43–0.90 | 0.01b |
| AA | 54 (17.76) | 62 (18.90) | 0.68 | 0.43–1.08 | 0.1 |
| P for trend | 0.016 | ||||
| (GA + AA) versus GG | 0.64 | 0.46–0.89 | 0.008b | ||
| AA versus (GG + GA) | 0.80 | 0.51–1.25 | 0.32 | ||
| XPF Pro662Ser (rs2020955) | |||||
| CC | 316 (99.06) | 325 (99.69) | Ref. | ||
| CT | 3 (0.94) | 0 (0.00) | N/A | ||
| TT | 0 (0.00) | 1 (0.31) | N/A | ||
| (CT + TT) versus CC | 2.68 | 0.27–26.67 | 0.4 | ||
| XPG Asp1104His (rs17655) | |||||
| GG | 188 (58.20) | 204 (60.90) | Ref. | ||
| GC | 118 (36.53) | 110 (32.84) | 1.12 | 0.80–1.57 | 0.52 |
| CC | 17 (5.26) | 21 (6.27) | 0.82 | 0.41–1.65 | 0.59 |
| P for trend | 0.66 | ||||
| (GC + CC) versus GG | 1.07 | 0.77–1.48 | 0.68 | ||
| CC versus (GG + GC) | 0.83 | 0.41–1.70 | 0.62 | ||
| XPC Lys939Gln (rs2228001) | |||||
| AA | 108 (33.96) | 112 (34.46) | Ref. | ||
| AC | 156 (49.06) | 165 (50.77) | 1.00 | 0.70–1.43 | 0.99 |
| CC | 54 (16.98) | 48 (14.77) | 1.15 | 0.71–1.88 | 0.56 |
| P for trend | 0.47 | ||||
| (AC + CC) versus AA | 1.03 | 0.74–1.45 | 0.84 | ||
| CC versus (AA + AC) | 1.16 | 0.73–1.82 | 0.53 | ||
| XPC Ala499Val (rs2228000) | |||||
| CC | 199 (62.78) | 194 (59.15) | Ref. | ||
| CT | 104 (32.81) | 116 (35.37) | 0.89 | 0.63–1.25 | 0.49 |
| TT | 14 (4.42) | 18 (5.49) | 0.75 | 0.36–1.58 | 0.45 |
| P for trend | 0.18 | ||||
| (CT + TT) versus CC | 0.87 | 0.62–1.21 | 0.40 | ||
| TT versus (CC + CT) | 0.64 | 0.29–1.44 | 0.28 | ||
| XPC intron 9 (PAT) | |||||
| PAT−/− | 107 (35.79) | 121 (39.03) | Ref. | ||
| PAT−/+ | 145 (48.49) | 134 (43.23) | 1.22 | 0.85–1.76 | 0.28 |
| PAT+/+ | 47 (15.72) | 55 (17.74) | 0.94 | 0.58–1.53 | 0.81 |
| P for trend | 0.81 | ||||
| (PAT−/+ and PAT+/+) versus PAT−/− | 1.14 | 0.81–1.60 | 0.46 | ||
| PAT+/+ versus (PAT−/− and PAT−/+) | 0.80 | 0.50–1.28 | 0.36 | ||
| RAD23B Ala249Val (rs1805329) | |||||
| CC | 199 (61.80) | 215 (64.18) | Ref. | ||
| CT | 112 (34.78) | 106 (31.64) | 1.19 | 0.85–1.67 | 0.32 |
| TT | 11 (3.42) | 14 (4.18) | 0.81 | 0.35–1.89 | 0.63 |
| P for trend | 0.51 | ||||
| (CT + TT) versus CC | 1.14 | 0.82–1.59 | 0.42 | ||
| TT versus (CC + CT) | 0.81 | 0.34–1.92 | 0.64 | ||
| CCNH Val270Ala (rs2266690) | |||||
| TT | 199 (61.61) | 211 (63.17) | Ref. | ||
| TC | 106 (32.82) | 108 (32.34) | 0.97 | 0.69–1.38 | 0.88 |
| CC | 18 (5.57) | 15 (4.49) | 1.09 | 0.52–2.28 | 0.83 |
| P for the trend | 0.89 | ||||
| (TC + CC) versus TT | 0.99 | 0.71–1.37 | 0.94 | ||
| CC versus (TT + TC) | 1.17 | 0.56–2.46 | 0.68 | ||
| ERCC6 Met1097Val (rs2228526) | |||||
| AA | 192 (59.26) | 223 (66.77) | Ref. | ||
| AG | 114 (35.19) | 94 (28.14) | 1.46 | 1.03–2.07 | 0.03 |
| GG | 18 (5.56) | 17 (5.09) | 1.19 | 0.58–2.44 | 0.64 |
| P for trend | 0.11 | ||||
| (AG + GG) versus AA | 1.42 | 1.02–1.98 | 0.04 | ||
| GG versus (AA + AG) | 0.99 | 0.47–2.09 | 0.98 | ||
| ERCC6 Arg1230Pro (rs4253211) | |||||
| GG | 261 (80.80) | 282 (84.18) | Ref. | ||
| GC | 61 (18.89) | 51 (15.22) | 1.38 | 0.90–2.12 | 0.14 |
| CC | 1 (0.31) | 2 (0.60) | 0.58 | 0.05–6.87 | 0.67 |
| P for trend | 0.19 | ||||
| (GC + CC) versus GG | 1.35 | 0.89–2.06 | 0.16 | ||
| CC versus (GG + GC) | 0.70 | 0.06–8.19 | 0.77 | ||
| XPA 5′UTR (rs1800975) | |||||
| AA | 62 (19.31) | 107 (32.33) | Ref. | ||
| AG | 139 (43.30) | 136 (41.09) | 1.78 | 1.18–2.69 | 0.006b |
| GG | 120 (37.38) | 88 (26.59) | 2.43 | 1.57–3.75 | <0.001b |
| P for trend | <0.001 | ||||
| (AG + GG) versus AA | 2.04 | 1.40–2.97 | <0.001b | ||
| GG versus (AA + AG) | 1.77 | 1.24–2.54 | 0.002b |
Adjusted for age, gender, smoking status, hypertension and body mass index.
Remain significant after FDR adjustment for multiple comparisons at 5% level.
Since more than one SNP of XPD, ERCC6 and XPC were included in the study, we assessed the association between haplotypes of these three genes and RCC risk (Table III). For XPD, the haplotype CA, which contained the variant C allele of the XPD Lys751Gln and the variant A allele of the XPD Asp312Asn, was associated with a reduced RCC risk (OR = 0.76; 95% CI: 0.58–1.00). For ERCC6, only three haplotypes were observed in the study population (AG, AC and GG). The haplotype GG (one variant G allele of the ERCC6 Met1097Val and one wild-type allele of ERCC6 Arg1230Pro) conferred a significantly increased RCC risk (OR = 1.34; 95% CI: 1.01–1.75). No association between XPC haplotype and RCC risk was observed (Table III). However, none of the associations remained significant after FDR adjustment for multiple comparisons at 5% level.
Table III.
XPD, ERCC6, XPC haplotype and RCC risk
| Haplotype | Cases | Controls | ||
| N (%) | N (%) | Adjusted OR (95% CI)a | P-value | |
| XPD Lys751-Asp312Asn | ||||
| AG | 343 (57.36) | 341 (53.45) | Ref. | |
| AA | 57 (9.53) | 65 (10.19) | 0.76 (0.52–1.17) | 0.22 |
| CG | 48 (8.03) | 42 (6.58) | 1.17 (0.74–1.84) | 0.5 |
| CA | 150 (25.08) | 190 (29.78) | 0.76 (0.58–1.00) | 0.052 |
| ERCC6 Met1097Val-Arg1230Pro | ||||
| AG | 433 (67.03) | 485 (72.60) | Ref. | |
| AC | 63 (9.75) | 55 (8.23) | 1.36 (0.91–2.04) | 0.13 |
| GG | 150 (23.22) | 128 (19.16) | 1.34 (1.01–1.75) | 0.04 |
| XPC Ala499Val-PAT-XPC Lys939Gln | ||||
| C − A | 209 (36.28) | 211 (35.76) | Ref. | |
| C + A | 5 (0.87) | 2 (0.34) | 2.51 (0.46–13.75) | 0.29 |
| T − A | 123 (21.35) | 137 (23.22) | 0.91 (0.66–1.25) | 0.55 |
| C − C | 11 (1.91) | 16 (2.71) | 0.75 (0.33–1.68) | 0.48 |
| C + C | 228 (39.58) | 224 (37.97) | 1.02 (0.77–1.34) | 0.9 |
Adjusted for age, gender, smoking status, hypertension and body mass index.
To test the hypothesis that multiple SNPs in the NER pathway may have an additive effect on RCC risk, we estimated the combined effect of these SNPs and then stratified the analyses by host characteristics (Table IV). For genes with multiple SNPs assayed (XPC, XPD, ERCC6), only one SNP was included in this analysis: XPD Asp312Asn, XPC intron 9 (PAT) and ERCC6 Met1097Val. We treated the minor allele at each locus as the adverse allele except for XPD Asp312Asn and tallied the total number of adverse alleles for each individual. For XPD Asp312Asn, because the minor C allele was associated with a significantly reduced RCC risk (Table II), the wild-type G allele was treated as the adverse allele. The number of adverse alleles for each individual ranged from 1 to 11. We categorized individuals based on the quartile distribution of the number of adverse alleles in controls (Table IV). Compared with the reference group (individuals with fewer than five adverse alleles), individuals with five (OR = 1.17; 95% CI: 0.71–1.93), six (OR = 1.66; 95% CI: 1.03–2.67), seven or more (OR = 1.85; 95% CI: 1.16–2.95) exhibited progressively increased risks of RCC, with a significant gene dosage effect (P for trend = 0.004). Stratified analyses showed that this significant dose–response trend was apparent in never smokers, men, older subjects (age ≥59 years), subjects with hypertension history and subjects with body mass index ≥25 (overweight or obese) (Table IV). For example, among hypertensive subjects, compared with reference group (less than five adverse alleles), those with five, six and seven or more adverse alleles had ORs of 2.21 (95% CI: 1.05–4.66), 3.61 (95% CI: 1.76–7.40) and 9.08 (95% CI: 1.56–6.06), respectively (P for trend <0.001); among overweigh and/or obese subjects, subjects carrying five, six and seven or more adverse alleles exhibited increased risks of 1.94 (95% CI: 1.09–3.46), 2.39 (95% CI: 1.33–4.28) and 2.67 (95% CI: 1.53–4.67), respectively (P for tend <0.001). There were significant interaction between NER genes and sex (P for interaction = 0.003), hypertension (P for interaction = 0.02) and obesity (P for interaction = 0.01) (Table IV).
Table IV.
NER pathway and RCC risk
| Number of adverse alleles | Cases/controls | Adjusted OR (95% CI)a | P-Value | P interaction |
| Overall | N/A | |||
| ≤4 | 77/111 | Ref. | ||
| 5 | 50/58 | 1.17 (0.71–1.93) | 0.53 | |
| 6 | 67/59 | 1.66 (1.03–2.67) | 0.04 | |
| >6 | 76/58 | 1.85 (1.16–2.95) | 0.01 | |
| P for trend | 0.004 | |||
| Smoking status | 0.52 | |||
| Never smoker | ||||
| ≤4 | 35/53 | Ref. | ||
| 5 | 28/32 | 1.48 (0.75–2.93) | 0.26 | |
| 6 | 35/25 | 2.14 (1.07–4.25) | 0.03 | |
| >6 | 40/22 | 2.68 (1.34–5.34) | 0.005 | |
| P for trend | 0.003 | |||
| Ever smoker | ||||
| ≤4 | 42/58 | Ref. | ||
| 5 | 21/26 | 0.96 (0.45–2.04) | 0.91 | |
| 6 | 32/34 | 1.30 (0.67–2.53) | 0.43 | |
| >6 | 36/36 | 1.29 (0.68–2.47) | 0.44 | |
| P for trend | 0.34 | |||
| Sex | 0.003 | |||
| Male | ||||
| ≤4 | 51/63 | Ref. | ||
| 5 | 26/44 | 0.70 (0.37–1.32) | 0.27 | |
| 6 | 51/34 | 1.95 (1.08–3.53) | 0.03 | |
| >6 | 52/30 | 2.13 (1.17–3.89) | 0.01 | |
| P for trend | 0.002 | |||
| Female | ||||
| ≤4 | 26/48 | Ref. | ||
| 5 | 24/14 | 2.95 (1.25–6.94) | 0.01 | |
| 6 | 16/25 | 1.18 (0.52–2.71) | 0.69 | |
| >6 | 24/28 | 1.56 (0.73–3.33) | 0.23 | |
| P for trend | 0.46 | |||
| Age | 0.68 | |||
| Age <59 | ||||
| ≤4 | 38/48 | Ref. | ||
| 5 | 25/28 | 0.94 (0.45–1.95) | 0.86 | |
| 6 | 33/28 | 1.46 (0.72–2.93) | 0.29 | |
| >6 | 32/27 | 1.42 (0.70–2.91) | 0.33 | |
| P for trend | 0.22 | |||
| Age ≥59 | ||||
| ≤4 | 39/63 | Ref. | ||
| 5 | 25/30 | 1.43 (0.72–2.87) | 0.31 | |
| 6 | 34/31 | 1.87 (0.97–3.62) | 0.06 | |
| >6 | 44/31 | 2.37 (1.25–4.48) | 0.01 | |
| P for trend | 0.005 | |||
| Hypertension | 0.02 | |||
| Yes | ||||
| ≤4 | 30/52 | Ref. | ||
| 5 | 28/22 | 2.21 (1.05–4.66) | 0.04 | |
| 6 | 41/20 | 3.61 (1.76–7.40) | < 0.001 | |
| >6 | 46/25 | 9.08 (1.56–6.06) | 0.001 | |
| P for trend | <0.001 | |||
| No | ||||
| ≤4 | 42/58 | Ref. | ||
| 5 | 20/36 | 0.70 (0.35–1.42) | 0.32 | |
| 6 | 24/39 | 0.85 (0.44–1.64) | 0.62 | |
| >6 | 28/33 | 1.20 (0.62–2.30) | 0.59 | |
| P for trend | 0.66 | |||
| Obesity | 0.01 | |||
| Normal (BMI <25 kg/m2) | ||||
| ≤4 | 23/26 | Ref. | ||
| 5 | 5/20 | 0.25 (0.08–0.82) | 0.02 | |
| 6 | 17/24 | 0.69 (0.28–1.68) | 0.41 | |
| >6 | 15/20 | 0.83 (0.33–2.10) | 0.70 | |
| P for trend | 0.81 | |||
| Overweight and obese (BMI ≥25 kg/m2) | ||||
| ≤4 | 49/84 | Ref. | ||
| 5 | 44/37 | 1.94 (1.09–3.46) | 0.03 | |
| 6 | 48/35 | 2.39 (1.33–4.28) | 0.003 | |
| >6 | 59/38 | 2.67 (1.53–4.67) | 0.001 | |
| P for trend | <0.001 |
Adjusted for age, gender, smoking status, hypertension and BMI (body mass index) where appropriate.
We then applied CART analysis to explore high-order gene–gene interactions. Figure 1 depicted the tree structure generated using the CART analysis. The final tree structure contained 10 terminal nodes, representing a range of low- versus high-risk subgroups as defined by the different combination of genotypes of NER SNPs. The first split was XPA 5′UTR, separating individuals into AA and AG + GG genotypes. Subsequent splits were XPD Asp312Asn, XPC Lys939Gln, RAD23B Ala249Val, ERCC1 3′UTR, ERCC6 Met1097Val and ERCC6 Arg1230Pro. To calculate ORs as defined by the 10 terminal nodes, we chose terminal node 1 (individuals carrying the AA genotype of the XPA and individuals with heterozygous or homozygous variants of the XPD Asp312Asn) as the reference group. The ORs of terminal nodes ranged from 1.25 (terminal node 2) to 7.63 (terminal node 10) (Table V). The highest risk group were individuals in terminal node 10 (homozygous GG of the XPA and at least one variant C allele of the ERCC6 Arg1230Pro) (OR = 7.63; 95% CI: 2.84–20.50) (Figure 1 and Table V).
Fig. 1.
CART analysis of NER pathway and RCC risk. ORs and 95% CIs (in parenthesis) are presented underneath each terminal node box. Please refer to Table II for name of the SNPs. For each SNP, ‘WW’ represents wild-type; ‘WM’ represents heterozygous genotype and ‘MM’ represents homozygous variant genotype. The total number of cases and controls does not add up to 325 and 335, respectively, because only subjects without missing SNP data were included in the analysis.
Table V.
CART terminal nodes and RCC risk
| Terminal nodea | Cases/controlsb | Case rate (%) | Adjusted ORc | P-value |
| 1 | 23/53 | 30.26 | Ref. | |
| 2 | 8/18 | 30.77 | 1.25 (0.45–3.41) | 0.67 |
| 3 | 18/16 | 52.94 | 3.92 (1.62–9.48) | 0.002 |
| 4 | 19/31 | 38.00 | 1.79 (0.81–3.98) | 0.15 |
| 5 | 43/38 | 53.09 | 3.31 (1.62–6.76) | 0.001 |
| 6 | 25/29 | 46.30 | 2.41 (1.11–5.25) | 0.03 |
| 7 | 25/14 | 64.10 | 5.67 (2.34–13.74) | <0.001 |
| 8 | 39/46 | 45.88 | 2.39 (1.19–4.82) | 0.01 |
| 9 | 37/18 | 67.27 | 6.37 (2.86–14.19) | <0.001 |
| 10 | 23/8 | 74.19 | 7.63 (2.84–20.50) | <0.001 |
Refer to Figure 1 for definition of each terminal node.
The total number of cases and controls does not add up to 325 and 335, respectively, because only subjects without missing SNP data were included in the analysis.
Adjusted for age, gender, smoking status, hypertension and body mass index.
Discussion
In this case–control study of RCC, we examined the individual and joint effects of 13 potentially functional SNPs in nine NER genes on RCC risk. Our data revealed that the XPA 5′UTR (rs1800975) and XPD Asp312Asn (rs1799793) exhibited significant associations with RCC risk. More importantly, we took a pathway-based polygenic approach to assess the combined effects of multiple SNPs involved in the NER pathway. Our results suggest that RCC risk increases with the increasing number of adverse alleles in the NER pathway. In stratified analyses, we showed that the effects of NER pathway genes on RCC risk may be modified by gender, hypertension history and obesity.
Disruption of genomic integrity contributes to malignant transformation and subsequent cancer development. The repair of DNA damage plays a key role in maintaining genomic integrity. NER is one of the major DNA repair pathways and is mainly responsible for the removal of bulky DNA adducts induced by chemical carcinogens (19,20). The NER pathway involves sequential reaction steps, including DNA damage recognition, incision of damaged DNA, repair of the gapped DNA and DNA ligation (8). XPC–hHR23B and XPA–RPA complexes recognize the initial DNA damage, whereas the ERCC6 cockayne syndrome B proteins recognize DNA lesions in the transcribed strand of an active gene (21–23). After the recruitment of transcription factor IIH complex (24), XPD and XPB unwind the DNA helix around the damaged site. The recruitment of the XPG– and XPF–ERCC1 complex is followed by the excision of a 24–32 bp segment containing the bulky adduct at the 3′ and 5′ ends of the damaged site. The resultant gap is filled by DNA synthesis and ligation.
Polymorphisms in NER genes have been extensively studied for their associations with cancers, such as lung cancer, bladder cancer, head and neck cancer, breast cancer and oral premalignant lesions (9–11,25). Only two studies have been conducted to study the association between NER genes and RCC risk. Hirata et al. (12) found no association between the two SNPs in NER pathway, the XPC Lys939GLn and ERCC1 codon 118, and RCC risk in a case–control study of 112 cases and 112 controls. In another case–control study conducted in Japan (13), which included 215 cases and 215 controls, the XPD Lys751Gln AC and CC genotype combined was associated with a reduced risk of RCC (OR = 0.41, 95% CI: 0.19–0.88), but the authors concluded that the results were non-significant after taking into account of multiple comparisons. The other two SNPs examined in the same study, the XPC Lys939Gln and the XPG Asp1104His, were not associated with RCC risk (13).
In individual SNP analysis of the current study, we found two significant results after adjustment for multiple comparisons. Specifically, the variant A allele of the XPD Asp312Asn (rs1799793) was associated with a reduced risk of RCC and the G allele of the XPA 5′UTR (rs1800975) was associated with an increased RCC risk. The two XPD SNPs (the XPD Asp312Asn and XPD Lys751Gln) are the most widely studied NER SNPs in cancer risk. A meta analysis of 3725 lung cancer cases and 4152 controls showed a significantly increased risk of lung cancer associated with the A allele of the Asp312Asn and the C allele of the Lys751Gln (26). No association was found for the two XPD polymorphisms with bladder cancer (27). Wang et al. (25) reported increased risk of oral premalignant lesions associated with the C allele of XPD Lys751Gln but no association was observed for the variant A allele of XPD Asp312Asn. The presence of one or two copies of the G allele of the XPA 5′UTR SNP was associated with a reduced lung cancer risk (18,28) but an increased risk for oral premalignant lesions (25).
The inconsistent results regarding the XPD and XPA SNPs may be due to different etiological factors in different cancers. It should be noted that the results derived from single SNP analysis tend to have high FDRs because multiple hypotheses are tested simultaneously and the probability of type I error rates increases with the number of tests. To take into account of the multiple comparison issue, we therefore applied the FDR adjustment to control for type I error rates. The significant findings for XPD Asp312Asn (rs1799793) and XPA 5′UTR (rs1800975) in our study were all adjusted for multiple comparisons. Nevertheless, the significance of the two SNPs in RCC etiology warrants further validation in independent populations.
The most important finding of this study is a significant trend of increased RCC risk with increasing numbers of adverse alleles in the NER pathway. The rationale behind this multigenic approach is that cancer is a complex disease that is influenced by the interplay between multiple genetic and environmental factors, and thus, it is unlikely that individual genes and/or SNPs would have substantial effects on the overall risk. We previously applied similar multigenic pathway-based approach to study the effects of genetic variants in NER pathway on the susceptibility of bladder cancer (9). Our data support the stronger prediction power of a multigenic approach over the single-candidate gene approach. Biologically, the combined effects of genetic variants in the NER pathway on cancer risk corroborated well with results from genotype–phenotype correlation study in that increasing number of adverse alleles in NER pathway was significantly correlated with decreasing DNA repair capacity phenotype (15).
In stratified analysis, the gene dosage effect of all the NER pathway genes, observed in this study, was more evident in never smokers, men, older subjects (age ≥59), subjects with a history of hypertension and overweight/obese subjects. However, the interaction was only significant for sex (P for interaction = 0.003), hypertension (P for interaction = 0.02) and obesity (P for interaction = 0.01).
A significant interaction was observed between NER pathway genes and sex with a gene dose effect only present in men but not in women. The gender disparity in RCC has been well described in literature. RCC is almost twice as common in men as in women, and men account for nearly two–thirds of deaths from RCC. A recent analysis of 1973–2004 Surveillance Epidemiology and End Results 17 registries database, which included 35,336 RCC patients (22,288 men), showed that men present with larger size tumors, higher stage and higher grade RCCs than women (29). The underlying mechanism of gender differences in RCC has yet to be elucidated. It is hypothesized that estrogen status, as well as microenvironment maintained by growth factors, inflammatory cytokines and adipocytokines may play a role (30–32). The interaction between NER pathway genes and sex observed in our study may be an indication of complex interactions between these modifying factors and DNA repair capacity in RCC etiology.
Both hypertension and obesity are well-established risk factors for RCC. Renal carcinogenesis may be promoted by deregulated lipid peroxidation and increased formation of reactive oxygen species in hypertensive and obese individuals (33). Hypertension can cause thickening of the arterioles supplying the kidney, leading to reduced blood flow and death of kidney cells (34). Decreased capacity to repair damaged kidney cells and increased cell proliferation due to cell loss may increase the probability of genetic mutation and cancer. Previous work suggests that body size and fat content may influence carcinogen–DNA adduct levels (5,6), which requires NER repair pathway. In animal models, increased lipid peroxidation of the proximal renal tubules has been linked to the chemical induction of renal tumors (35,36). Adiposity is related to increased levels of insulin and insulin-like growth factor 1, which are known to promote carcinogenesis (37–40). Obesity may also increase risk of RCC through elevation of estrogen levels (41) or through increased glomerular filtration rate and renal plasma flow (42). Our data suggest that decreased DNA repair capacity, as reflected by the carrying of high number of adverse alleles in NER pathway, may place hypertensive and obese individuals at higher risk of RCC.
Our results suggest that the increased risk of RCC associated with NER genetic variants was evident in never smokers but not in ever smokers, in older subjects but not in younger subjects, although the interactions did not reach statistical significance. One possible explanation for the absence of genetic effects in ever smokers is that the genetic effects may be overwhelmed by smoking and similar observations have been reported in previous studies (43,44). It is well known that DNA damage accumulation with age due to the decreased immune function that makes older subjects more susceptible to carcinogens present in the environment (45). Therefore, older subjects with impaired DNA repair machinery may be at a higher risk than younger subjects upon adverse environmental exposures.
To explore the high-order interactions among the NER SNPs, we applied the CART analysis to define high- versus low-risk subgroups. Consistent with the main effect derived from the logistic regression analysis, the XPD Asp312Asn (rs1799793) and the XPA 5′UTR (rs1800975) polymorphisms were also identified in the CART analysis as splitting variables. Moreover, subgroups of individuals with a range of RCC risk profile were identified through CART modeling based on combinations of NER genotypes. Because of the moderate sample size of this study, the number of subjects becomes small in terminal nodes, therefore these results should be interpreted with caution.
The selection of SNPs in this study is still limited and is based on prior knowledge of potential functional significance of SNPs that have been related to cancer risk. A more comprehensive tagging SNP-based approach would provide more complete information about the associations of NER genes and RCC risk.
In conclusion, this is the largest study to examine RCC risk in association with genetic variants of candidate genes in the NER pathway. Our results strongly support that common sequence variants of the NER pathway genes predispose susceptible individuals to increased risk of RCC and that the association may be modified by history of hypertension, obesity and gender. Due to the moderate sample size of the current study, the results should be interpreted with caution, especially for gene–gene and gene–environment interaction. Thus, these results need to be replicated in larger studies.
Funding
National Cancer Institute (CA 74880 and CA 91846).
Acknowledgments
Conflict of Interest Statement: None declared.
Glossary
Abbreviations
- CART
classification and regression tree
- CI
confidence interval
- FDR
false discovery rate
- NER
nucleotide excision repair
- OR
odds ratio
- RCC
renal cell carcinoma
- SNP
single nucleotide polymorphism
- UTR
untranslated region
References
- 1.Jemal A, et al. Cancer statistics, 2008. CA Cancer J. Clin. 2008;58:71–96. doi: 10.3322/CA.2007.0010. [DOI] [PubMed] [Google Scholar]
- 2.Setiawan VW, et al. Risk factors for renal cell cancer: the multiethnic cohort. Am. J. Epidemiol. 2007;166:932–940. doi: 10.1093/aje/kwm170. [DOI] [PubMed] [Google Scholar]
- 3.Lipworth L, et al. The epidemiology of renal cell carcinoma. J. Urol. 2006;176:2353–2358. doi: 10.1016/j.juro.2006.07.130. [DOI] [PubMed] [Google Scholar]
- 4.Dhote R, et al. Risk factors for adult renal cell carcinoma: a systematic review and implications for prevention. BJU Int. 2000;86:20–27. doi: 10.1046/j.1464-410x.2000.00708.x. [DOI] [PubMed] [Google Scholar]
- 5.Godschalk RW, et al. Body mass index modulates aromatic DNA adduct levels and their persistence in smokers. Cancer Epidemiol. Biomarkers Prev. 2002;11:790–793. [PubMed] [Google Scholar]
- 6.Rundle A, et al. The association between benzo[a]pyrene-DNA adducts and body mass index, calorie intake and physical activity. Biomarkers. 2007;12:123–132. doi: 10.1080/13547500601010418. [DOI] [PubMed] [Google Scholar]
- 7.Christmann M, et al. Mechanisms of human DNA repair: an update. Toxicology. 2003;193:3–34. doi: 10.1016/s0300-483x(03)00287-7. [DOI] [PubMed] [Google Scholar]
- 8.Sancar A, et al. Molecular mechanisms of mammalian DNA repair and the DNA damage checkpoints. Annu. Rev. Biochem. 2004;73:39–85. doi: 10.1146/annurev.biochem.73.011303.073723. [DOI] [PubMed] [Google Scholar]
- 9.Wu X, et al. Bladder cancer predisposition: a multigenic approach to DNA-repair and cell-cycle-control genes. Am. J. Hum. Genet. 2006;78:464–479. doi: 10.1086/500848. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Michiels S, et al. Polymorphism discovery in 62 DNA repair genes and haplotype associations with risks for lung and head and neck cancers. Carcinogenesis. 2007;28:1731–1739. doi: 10.1093/carcin/bgm111. [DOI] [PubMed] [Google Scholar]
- 11.Crew KD, et al. Polymorphisms in nucleotide excision repair genes, polycyclic aromatic hydrocarbon-DNA adducts, and breast cancer risk. Cancer Epidemiol. Biomarkers Prev. 2007;16:2033–2041. doi: 10.1158/1055-9965.EPI-07-0096. [DOI] [PubMed] [Google Scholar]
- 12.Hirata H, et al. Polymorphisms of DNA repair genes are associated with renal cell carcinoma. Biochem. Biophys. Res. Commun. 2006;342:1058–1062. doi: 10.1016/j.bbrc.2006.02.030. [DOI] [PubMed] [Google Scholar]
- 13.Sakano S, et al. The association of DNA repair gene polymorphisms with the development and progression of renal cell carcinoma. Ann. Oncol. 2007;18:1817–1827. doi: 10.1093/annonc/mdm337. [DOI] [PubMed] [Google Scholar]
- 14.Olson SH, et al. Evaluation of random digit dialing as a method of control selection in case-control studies. Am. J. Epidemiol. 1992;135:210–222. doi: 10.1093/oxfordjournals.aje.a116273. [DOI] [PubMed] [Google Scholar]
- 15.Lin J, et al. Mutagen sensitivity and genetic variants in nucleotide excision repair pathway: genotype-phenotype correlation. Cancer Epidemiol. Biomarkers Prev. 2007;16:2065–2071. doi: 10.1158/1055-9965.EPI-06-1041. [DOI] [PubMed] [Google Scholar]
- 16.Benjamini Y, et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. 1995;57:289–300. [Google Scholar]
- 17.Zhang H, et al. Recursive Partitioning in the Health Sciences. New York: Springer; 1999. [Google Scholar]
- 18.Wu X, et al. XPA polymorphism associated with reduced lung cancer risk and a modulating effect on nucleotide excision repair capacity. Carcinogenesis. 2003;24:505–509. doi: 10.1093/carcin/24.3.505. [DOI] [PubMed] [Google Scholar]
- 19.Aboussekhra A, et al. Mammalian DNA nucleotide excision repair reconstituted with purified protein components. Cell. 1995;80:859–868. doi: 10.1016/0092-8674(95)90289-9. [DOI] [PubMed] [Google Scholar]
- 20.Wood RD. DNA repair. Variants on a theme. Nature. 1999;399:639–640. doi: 10.1038/21323. [DOI] [PubMed] [Google Scholar]
- 21.He Z, et al. RPA involvement in the damage-recognition and incision steps of nucleotide excision repair. Nature. 1995;374:566–569. doi: 10.1038/374566a0. [DOI] [PubMed] [Google Scholar]
- 22.Li L, et al. An interaction between the DNA repair factor XPA and replication protein A appears essential for nucleotide excision repair. Mol. Cell. Biol. 1995;15:5396–5402. doi: 10.1128/mcb.15.10.5396. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kamiuchi S, et al. Translocation of Cockayne syndrome group A protein to the nuclear matrix: possible relevance to transcription-coupled DNA repair. Proc. Natl Acad. Sci. USA. 2002;99:201–206. doi: 10.1073/pnas.012473199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.De Boer J, et al. Nucleotide excision repair and human syndromes. Carcinogenesis. 2000;21:453–460. doi: 10.1093/carcin/21.3.453. [DOI] [PubMed] [Google Scholar]
- 25.Wang Y, et al. Nucleotide excision repair pathway genes and oral premalignant lesions. Clin. Cancer Res. 2007;13:3753–3758. doi: 10.1158/1078-0432.CCR-06-1911. [DOI] [PubMed] [Google Scholar]
- 26.Hu Z, et al. DNA repair gene XPD polymorphism and lung cancer risk: a meta-analysis. Lung Cancer. 2004;46:1–10. doi: 10.1016/j.lungcan.2004.03.016. [DOI] [PubMed] [Google Scholar]
- 27.Schabath MB, et al. Polymorphisms in XPD exons 10 and 23 and bladder cancer risk. Cancer Epidemiol. Biomarkers Prev. 2005;14:878–884. doi: 10.1158/1055-9965.EPI-04-0235. [DOI] [PubMed] [Google Scholar]
- 28.Park JY, et al. Polymorphisms of the DNA repair gene xeroderma pigmentosum group A and risk of primary lung cancer. Cancer Epidemiol. Biomarkers Prev. 2002;11:993–997. [PubMed] [Google Scholar]
- 29.Aron M, et al. Impact of gender in renal cell carcinoma: an analysis of the SEER database. Eur. Urol. 2008;54:133–142. doi: 10.1016/j.eururo.2007.12.001. [DOI] [PubMed] [Google Scholar]
- 30.Böttner A, et al. Gender differences of adiponectin levels develop during the progression of puberty and are related to serum androgen levels. J. Clin. Endocrinol. Metab. 2004;89:4053–4061. doi: 10.1210/jc.2004-0303. [DOI] [PubMed] [Google Scholar]
- 31.Horiguchi A, et al. Increased serum leptin levels and over expression of leptin receptors are associated with the invasion and progression of renal cell carcinoma. J. Urol. 2006;176:1631–1635. doi: 10.1016/j.juro.2006.06.039. [DOI] [PubMed] [Google Scholar]
- 32.Naugler WE, et al. Gender disparity in liver cancer due to sex differences in MyD88-dependent IL-6 production. Science. 2007;317:121–124. doi: 10.1126/science.1140485. [DOI] [PubMed] [Google Scholar]
- 33.Gago-Dominguez M, et al. Lipid peroxidation: a novel and unifying concept of the etiology of renal cell carcinoma (United States) Cancer Causes Control. 2002;13:287–293. doi: 10.1023/a:1015044518505. [DOI] [PubMed] [Google Scholar]
- 34.Shapiro JA, et al. Hypertension, antihypertensive medication use, and risk of renal cell carcinoma. Am. J. Epidemiol. 1999;5149:521–530. doi: 10.1093/oxfordjournals.aje.a009848. [DOI] [PubMed] [Google Scholar]
- 35.Toyokuni S, et al. Induction of a wide range of C(2-12) aldehydes and C(7-12) acyloins in the kidney of Wistar rats after treatment with a renal carcinogen, ferric nitrilotriacetate. Free Radic. Biol. Med. 1997;22:1019–1027. doi: 10.1016/s0891-5849(96)00489-3. [DOI] [PubMed] [Google Scholar]
- 36.Toyokuni S, et al. Formation of 4-hydroxy-2-nonenal-modified proteins in the renal proximal tubules of rats treated with a renal carcinogen, ferric nitrilotriacetate. Proc. Natl Acad. Sci. USA. 1994;91:2616–2620. doi: 10.1073/pnas.91.7.2616. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Aaronson SA. Growth factors and cancer. Science. 1991;254:1146–1153. doi: 10.1126/science.1659742. [DOI] [PubMed] [Google Scholar]
- 38.Kaaks R, et al. Energy balance and cancer: the role of insulin and insulin-like growth factor-I. Proc. Nutr. Soc. 2001;60:91–106. doi: 10.1079/pns200070. [DOI] [PubMed] [Google Scholar]
- 39.Cheung CW, et al. The roles of IGF-I and IGFBP-3 in the regulation of proximal tubule, and renal cell carcinoma cell proliferation. Kidney Int. 2004;65:1272–1279. doi: 10.1111/j.1523-1755.2004.00535.x. [DOI] [PubMed] [Google Scholar]
- 40.Kellerer M, et al. Insulin- and insulin-like growth-factor-I receptor tyrosine-kinase activities in human renal carcinoma. Int. J. Cancer. 1995;62:501–507. doi: 10.1002/ijc.2910620502. [DOI] [PubMed] [Google Scholar]
- 41.Calle EE, et al. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat. Rev. Cancer. 2004;4:579–591. doi: 10.1038/nrc1408. [DOI] [PubMed] [Google Scholar]
- 42.Hall JE, et al. Mechanisms of hypertension and kidney disease in obesity. Ann. N. Y. Acad. Sci. 1999;892:91–107. doi: 10.1111/j.1749-6632.1999.tb07788.x. [DOI] [PubMed] [Google Scholar]
- 43.Jin X, et al. Higher lung cancer risk for younger African-Americans with the Pro/Pro p53 genotype. Carcinogenesis. 1995;16:2205–2208. doi: 10.1093/carcin/16.9.2205. [DOI] [PubMed] [Google Scholar]
- 44.Hazra A, et al. Death receptor 4 and bladder cancer risk. Cancer Res. 2003;63:1157–1159. [PubMed] [Google Scholar]
- 45.Perera FP, et al. Associations between carcinogen-DNA damage, glutathione S-transferase genotypes, and risk of lung cancer in the prospective Physicians’ Health Cohort Study. Carcinogenesis. 2002;23:1641–1646. doi: 10.1093/carcin/23.10.1641. [DOI] [PubMed] [Google Scholar]